Cancer immunity marker RNA expression levels across gynecologic cancers: Implications for immunotherapy

Background: Our objective was to characterize cancer immunity marker expression in gynecologic cancers and compare immune landscapes between gynecologic tumor subtypes and with non-gynecologic solid tumors. Methods: RNA expression levels of 51 cancer-immunity markers were analyzed in patients with gynecologic cancers vs. non-gynecologic cancers, and normalized to a reference population of 735 control cancers, ranked from 0–100, and categorized as low (0–24), moderate (25–74), or high (75–100) percentile rank. Results: Of the 72 patients studied, 43 (60%) had ovarian, 24 (33%) uterine, and 5 (7%) cervical cancer. No two immune profiles were identical according to expression rank (0–100) or rank level (low, moderate, or high). Patients with cervical cancer had significantly higher expression level ranks of immune activating, pro-inflammatory, tumor infiltrating lymphocyte markers and checkpoints than patients with uterine or ovarian cancer (p<0.001 for all comparisons). However, there were no significant differences in immune marker expression between uterine and ovarian cancers. Tumors with PD-L1 TPS =>1% versus 0% had significantly higher expression levels of pro-inflammatory markers (58 vs. 49%, p=0.0004). Compared to patients with non-gynecologic cancers, more patients with gynecologic cancers express high levels of IDO-1 (44 vs. 13%, p<0.001), LAG3 (35 vs. 21%, p=0.008) and IL10 (31 vs. 15%, p=0.002.) Conclusions: Patients with gynecologic cancers have complex and heterogeneous immune landscapes that are distinct from patient to patient and from other solid tumors. High levels of IDO1 and LAG3 suggest that clinical trials with IDO1 inhibitors or LAG3 inhibitors, respectively, may be warranted in gynecologic cancers.


Introduction
Immunotherapies have revolutionized the treatment of solid tumors, with e cacy, even in patients with metastatic disease and multiple lines of prior therapy 1 . Importantly, there is a growing body of literature to support the role of the immune system in the development, response to treatment, and behavior of gynecologic cancers and, hence, immunotherapy has emerged as an area of special focus for these malignancies.
Recent data suggest endometrial cancers can be classi ed into subtypes that may inform precision genomic and immunotherapies 2 . Endometrial cancers have been found to have one of the highest programmed-death ligand 1 (PD-L1) expression levels 3 and prevalence of microsatellite instability (MSI) compared to other cancer types 4 . High tumor mutational burden (TMB) and neoantigen load from polymerase epsilon (POLE) mutant and MSI-high (MSI-H) malignancies, each of which can correlate with subgroups of endometrial cancer, associate with increased tumor-in ltrating lymphocytes (TIL) 5 and improved survival 6 . For these reasons, perhaps, immunotherapy strategies have seen clinical e cacy in endometrial cancers. In 2017, the Food and Drug Association (FDA) issued its rst tissue-agnostic approval for pembrolizumab, a programmed cell death protein-1 (PD-1) signal pathway inhibitor, in solid tumors with MSI-H or mismatch repair (MMR) de ciency 7,8,9 , and eventually to all tumors with high TMB ( 10 mutations/megabase) 10 . The combination of pembrolizumab and a multi-kinase inhibitor lenvatinib was next approved in 2019 for patients with MMR-pro cient endometrial tumors 11 . In April 2021, the FDA granted accelerated approval for dostarlimab (anti-PD-1) for the treatment of adult patients with de cient MMR recurrent or advanced endometrial cancer 12 . Ultimately, in March 2022, the FDA also granted approval of pembrolizumab for patients with MSI-H/dMMR advanced endometrial cancer, who have disease progression following prior systemic therapy based on the updated results of KEYNOTE-158 13 . Persistent human papilloma virus (HPV) infection is believed to cause 99.7% of invasive cervical cancers, making cervical cancer another disease site anticipated to be responsive to immunotherapy, since viral neoantigens may be immunogenic 14 . Moreover, HPV infections have been found to increase PD-L1 expression 15 , thereby creating an immune-privileged environment 16 . In the phase III, KEYNOTE 826 trial, pembrolizumab given upfront with chemotherapy improved both overall survival (OS) and progressionfree survival (PFS) in patients with PD-L1 positive cervical cancer 17 , supporting the FDA approval of pembrolizumab in these patients. For PD-L1 negative cervical cancer patients, the combination of ipilimumab (anti-CTLA4) and nivolumab (anti-PD1) demonstrated an objective response rate of 31.6% 18 .
In patients with ovarian cancer, the bene t of immunotherapy is less clear. Given ovarian cancer patients with increased TILs have longer survival 19 , it would seem logical for immunotherapy to have a successful role. However, the response rate to single-agent nivolumab in platinum-resistant patients was only 15%, with no correlation between clinical response and PD-L1 expression level 20 . The incorporation of avelumab, an anti-PD-L1 monoclonal antibody, into frontline chemotherapy in patients with ovarian cancer also failed to show e cacy 21 . While a higher objective response rate (ORR) has been observed in ovarian tumors with PD-L1 immunohistochemistry (IHC) combined positive score (CPS) 10% compared to patients with CPS <10% (17.5 v 8%) 22 , the ORRs for both are low. Furthermore, in the placebo controlled, randomized, phase 3 IMagyn050/GOG 3015/ENGOT-OV39 trial, the addition of atezolizumab to platinumbased combination chemotherapy and bevacizumab failed to show an improvement in oncologic outcomes 23 . Overall, there is an impression that the immunosuppressive tumor microenvironment in patients with ovarian cancer is di cult to overcome with a single-agent approach.
There is a strong rationale for using immunotherapy in patients with gynecologic tumors, but the e cacy of this approach is limited in part by our understanding of the biologic/immune underpinning of these cancers. In our study, we utilized RNA-seq immune gene expression to begin describing the immune pressures in ovarian, uterine, and cervical tumors. Our ndings may begin to explain the differential responses to immunotherapies between gynecologic disease sites and compared to non-gynecologic malignancies.

Materials And Methods
Patients Cancer-immunity markers among 72 eligible, consecutive patients with gynecologic solid cancers seen at the University of California San Diego Moores Cancer Center for Personalized Therapy were evaluated at a Clinical Laboratory Improvement Amendments (CLIA)-licensed and College of American Pathologist (CAP)-accredited clinical laboratory, OmniSeq (https://www.omniseq.com/). All investigations followed the Institutional Review Board protocol for data collection (Pro le Related Evidence Determining Individualized Cancer Therapy, NCT02478931) and for any investigational interventions for which the patients consented.
PD-L1 protein expression was measured using the tumor proportional score (TPS) (percentage of viable tumor cells showing partial or complete membrane IHC staining of any intensity). The expression of PD-L1 on the surface of tumor cells was assessed via the Dako Omnis platform (Agilent, Santa Clara, CA) using the anti-PD-L1 22C3 pharmDx antibody (Agilent, Santa Clara, CA), and expression levels were scored as per manufacturer guidelines.
Immunoprints refer to an overview of each individual patient's immune pro le. Each column represents a unique patient and each row represents the immune markers investigated. The corresponding cell is colored according to RNA expression level rank: high, moderate, or low.
An immunogram is an overview of immune pro les across patient cohorts. Immune markers were categorized according to their function, to one of six categories: immune checkpoints, immune escape/ anti-in ammatory markers or macrophage associated markers, tumor in ltrating lymphocyte markers, pro-in ammatory markers, and T-cell primed markers 25 . Each immune marker category makes up each "spoke" of the radar plot. The length of each spoke corresponds to the average RNA transcript rank expression level (0-100). The mean RNA expression level rank of immune factors in each immune marker category was plotted on each spoke and connected to visually compare the "immune pressures" between disease sites and cohorts 26-29 .

Endpoints and statistical methods
Patient characteristics and the pattern of cancer-immunity markers were summarized by descriptive statistics. Proportions were compared using Fisher's Exact test. Means were compared using Student's t test. The correlation between the disease sites and comprehensive expression patterns was evaluated through principal component analysis (PCA) and quanti ed by calculating silhouette scores. Statistical analyses were performed using SPSS version 28.0 (Chicago, IL, USA), Microsoft Excel version 16.49, and R 3.6.1 (R Foundation for Statistics Computing, Vienna, Austria). A p value <0.05 was considered signi cant.

Patient and tumor characteristics
Clinicopathologic features of our cohort of 72 patients with gynecologic cancers are depicted in Table 1.
Forty-three patients (60%) had ovarian cancer; 24 (33%) uterine cancer; and 5 (7%) cervix cancer. Of the cohort of patients with ovarian cancer, 52% were less than 65 years of age, no tumors had TMB >10 mutations/megabase, and 49% had tumors with a PD-L1 TPS of >1%. Of the cohort of patients with uterine cancer, 46% were less than 65 years of age, 8% (N= 2) had tumors with TMB >10 mutations/megabase (both of which were MSI-High), and 37% had tumors with a with a PD-L1 TPS of >1%. Of the small cohort of patients with cervix cancer (N=5), the majority (N=4) were less than 65 years of age, no patient had a tumor with TMB >10 mutations/megabase and 2 patients had a PD-L1 TPS score that was positive.
The immune pro les of the patients varied between patients and disease and are shown in Multiple potentially actionable immune inhibitory and immune stimulatory markers were expressed in gynecologic malignancies Both immune suppressive markers and immune activating markers were examined. High levels of immune suppressive markers are relevant as they can be counteracted by agents that inhibit them. Low levels of immune activating markers are relevant as they would need to be augmented in a therapeutic setting. Figure 1a summarizes the percentage of patients with gynecologic cancers that had high RNA expression of each immune marker. Among immune suppressive markers, patients with gynecologic cancers were most likely to have high expression of IDO1 (44%), LAG3 (35%), IL10 (29%), VISTA (24%), CCR2 (21%), CCL2 (21%), PD1 (19%). Among immune activating markers, patients with gynecologic cancers were most likely to have high expression of MX1 (42%), DDX58 (39%), ICOSL (33%), TNF (32%), STAT1 (32%), CXCL10 (29%), CD40 (29%), and OX40L (28%). Among tumor in ltrating lymphocyte markers, patients with gynecologic cancers were most likely to have high expression of CD8 (22%) and KLRD1 (21%). Figure  IDO1, an immune suppressive marker, is the transcript most frequently highly expressed in gynecologic cancers Figure 1a demonstrates that 44.4% of gynecologic cancers had high expression of the immunosuppressive IDO1 RNA. IDO1 was highly expressed in 37% of ovarian cancers, 50% of uterine cancers, and 4 of 5 patients with cervical cancer (Figures 1b, c, d).
LAG3, an immunosuppressive marker, is highly expressed in ovarian and uterine cancers LAG3 was the second most frequently expressed immunosuppressive marker in ovarian (30% of cases) and in uterine cancer (42% of cases). In cervical cancer, it was highly expressed in 2 of 5 patients (Figures  1a-3d).
A signi cantly higher proportion of patients with gynecologic cancers express high levels of IDO-1, LAG3 and IL10 compared to non-gynecologic solid tumors.
For immune inhibitory factors, the proportion of patients with high expression in gynecologic malignancies (N=72) was compared to those with high expression in non-gynecologic malignancies (N=442 patients pro led at UCSD). Potential drugs would be those agents that suppress high expression of immune inhibition.
Thirty-two of 72 (44%) patients with gynecologic cancers expression high levels of IDO-1 compared to 13% of patients with non-gynecologic cancers (p<0.0001) ( Table 2). When IDO-1 is high, it can potentially be targeted with IDO1 inhibitors such as epacadostat, indoximod, etc.
Patients with gynecologic cancers also have high expression levels of LAG3 (35% vs. 21%, p=0.008), compared to patients with non-gynecologic solid tumors. When LAG3 is high, it is potentially targetable with LAG3 inhibitors such as relatlimab, BI 754111, LAG525 and MK-4280.
Patients with gynecologic cancers also have high expression levels of IL10 compared to those with nongynecologic cancers (31% vs. 15%, p=0.002). When IL10 levels are high, they are potentially targetable with IL10 inhibitors such as MK-1966 (see Supplemental Table 1).
A signi cantly smaller proportion of patients with gynecologic cancers express high levels of ADORA2A as compared to other cancers (6.9% versus 23.1%; p=0.002), which may mean less success with using drugs that target this protein in this patient population.
For immune-stimulatory factors, we similarly compared the proportion of patients with low expression in those with gynecologic malignancies to those with non-gynecologic malignancies. Potential drugs would be agents that stimulate the speci c immune stimulatory function. There were no statistically signi cant differences in proportion of patients with low expression of immune stimulatory factors (ie. IFNG, ICOS, CD40LG, IL1B, CD137, CITR, TNF, OX40L, OX40, ICOSLG) between those with gynecologic cancers and those with non-gynecologic cancers.
Immune marker RNA expression level differed from patient to patient amongst 72 individuals with gynecologic cancer Fifty-one immune markers were investigated. Figure 3a depicts an immunoprint of RNA expression levels (low=<25%, moderate 25-74%, or high >=75%) across immune markers for each individual patient studied. Figure 3b depicts an immunoprint of only high RNA expression levels (>=75%) across immune markers. There were no two gynecologic cancers with identical immune portfolios according to immune RNA expression rank numbers (1-100) or according to immune expression rank levels (low, moderate, or high).
Immune marker RNA expression level could not be clustered based on uterine of ovarian histology.
A cluster plot withprincipal component analysis (Figure 4) that summarized 51-dimensional data corresponding to the 51 different cancer-immunity markers on a two-dimensional eld demonstrated that the distribution cancer immunity marker RNA expression for patients with uterine and ovarian cancer were largely overlapping, which suggests that the pattern of RNA expression pattern of 51 markers were not associated with disease site. (Since there were only 5 patients with cervical cancer, these tumors were not mapped on this plot). The nding was validated by the calculation of silhouette score, which represents the variation within clusters compared to the variation between clusters. The silhouette score was 0.011, while the silhouette score of one million times of randomization of cancer sites in the same cohort was calculated as 0.00 ± 0.012. (mean and error). This suggests that the comprehensive expression patterns of 51 genes did not correlate with disease site.
Patients with cervix cancer, but not those with ovarian or uterine cancer, have higher RNA expression level ranks of immune checkpoint, TIL, pro-in ammatory and T-cell primed markers than approximately 70% of patients with other cancer types.
An immunogram was generated by plotting the average RNA expression level rank of immune markers for each corresponding immune marker category on a radar plot (Figure 5a). Although the sample size is small, cervical cancers have a mean RNA expression rank level of immune checkpoint, TIL, proin ammatory and T-cell primed markers higher than ovarian and uterine cancers (p<0.0001 for all comparisons) and higher than approximately 70% of all other cancer types. Cervix cancers have an average RNA expression rank level of immune escape/anti-in ammatory markers (51% rank), and slightly less than average (47%) rank level of macrophage associated markers compared to other cancer types.
Ovarian and uterine cancers have lower than average (<50% rank level) expression of almost all immune categories including immune checkpoints, TIL, T-cell primed and macrophage associated markers compared to other cancer types. Ovarian cancers have a slightly higher than average RNA expression level (52%) of pro-in ammatory markers compared to other cancer types. There were no signi cant differences in mean expression levels of any immune marker categories between uterine and ovarian cancers.
Patients with PD-L1 TPS >= 1% have signi cantly higher mean RNA expression rank levels of proin ammatory markers than those with PD-L1 TPS of 0%.
An immunogram was next generated to depict mean RNA expression levels of each immune category by PD-L1 IHC status (Figure 5b). Patients with PD-L1 IHC TPS >=1% had signi cantly higher expression levels of pro-in ammatory markers compared to those with PD-L1 IHC TPS 0% tumors (58 vs. 49%, p=0.0004). Immunograms of each disease site by PDL1 status are shown in Supplemental Figure 1a-c.

Discussion
In our report, we characterize the immune pro les of patients with gynecologic cancers using 51 RNA transcript levels associated with the cancer immunity cycle. Notably, we found that no two tumors have an identical immune pro le. This nding highlights the complexities of immune interactions, as well as the need to interrogate each tumor in the context of choosing precision immunotherapeutics. Contemporary work with The Cancer Genome Atlas (TCGA) and other large databases, integrating immunogenomics using powerful computational science have started to describe six distinct clusters of immune subtypes with potential implications for cancer treatment, though the prior work also demonstrated signi cant interpatient immune landscape heterogeneity 30,31 . These unique immune pro les again highlight the opposing immune pressures that affect the tumor microenvironment. Moreover, patient and tumor heterogeneity have implications for treatment choice. It remains challenging to design clinical trials in the context of big data and next generation sequencing, the latter which also suggests that the molecular genomic pro le of metastatic tumors differ from patient to patient; however, emerging observations, at least in the precision genomics space, suggest that treatment regimens based on analyzing each patient's cancer genomic landscape using next generation sequencing may improve patient outcomes 32,33,34 . A similar paradigm of individual tumor immune pro le interrogation and matching cancers with the right immunotherapy may be required.
We used an immunogram as the framework to describe the interacting immune pressures mentioned previously 26 . Of interest, patients with cervix cancer (though the numbers of patients were small) have higher RNA expression levels of immune-activating factors than immune-inhibitory factors, which may signify a generally "hotter" tumor. Moreover, patients with cervix cancer have higher RNA expression levels of immune-activating factors compared to many other types of solid tumors including uterine and ovarian cancers, consistent with larger studies utilizing the TCGA databases of diverse solid tumors 29 . Patients with uterine and ovarian cancers have a slightly lower than average (<50% expression rank level) RNA level of both immune activating and immune inhibitory markers. These ndings may begin to explain the success of incorporating pembrolizumab (anti-PD-1) in the treatment of metastatic cervical cancer, and the comparatively lackluster response with the incorporation of immune checkpoints for patients with ovarian cancer 17,35 .
Upregulation of alternative checkpoints may also explain why some patients do not respond to anti-PD-L1/anti-PD-1 agents 24 . An analysis of immune gene expression in patients with cervix cancer using the TCGA and GEO databases was able to delineate high-versus low-risk immune pro les that reliably predicted survival. The high-risk group was characterized by over-expression of macrophages and mast cells, probably due to their ability to promote lymphangiogenesis and angiogenesis 36 . In our study, we found higher expression levels of pro-in ammatory markers in patients with PD-L1 IHC positive tumors across all three disease sites, but lower levels of other immune markers. This may begin to explain why PD-L1 IHC serves as a limited therapeutic biomarker as it likely captures only one aspect of the immune microenvironment. The IHC 22C3 antibody only measures tumor PD-L1 whereas IHC SP142 measures tumoral and immune cell PD-L1. In patients with cervical cancer, those with PDL1-positive tumors had higher levels of TIL markers as well. However, in patients with ovarian and uterine cancers, PD-L1 positivity did not correlate with higher TIL markers. Perhaps this again explains, at least in part, the unique success of using checkpoint inhibitors in patients with cervical cancer, but not in ovarian cancers.
On the other hand, uterine cancers are also sensitive to anti-PD1 checkpoint blockade, and this might be due to other factors, such as the presence of MSI-High, high TMB and POLE mutations 2,37,38 .
We also investigated over-expression of immune inhibitory factors and under-expression of immune activating factors in gynecologic vs. non-gynecologic tumors with the hypothesis that drugs that block inhibition and or stimulate activation factors may be good candidates for therapeutics. Of interest, patients with gynecologic cancers had higher IDO1 RNA expression levels compared to other cancer types, with about 44% of gynecologic cancers expressing high IDO1 compared to less than 13% of other cancer types (p<0.001). IDO1 is the rst and rate-limiting enzyme in the degradation of tryptophan, which is expressed in cancer cells or draining lymph nodes 39 . When IDO1 is high, it can potentially be targeted with IDO1 inhibitors such as epacadostat, indoximod, etc. Previously, bioinformatics analysis of IDO1 immune function in gynecologic cancers using databases such as Oncomine, GEPIA etc. also found overexpression of IDO1 RNA as well as protein expression in gynecologic tumors 40 . Though phase 1 studies showed promising tolerance and response rates to the IDO inhibitor epacadostat 41 , phase 2 studies comparing the IDO inhibitor epacadostat against tamoxifen in ovarian cancer types did not show signi cantly improved e cacy 42 . IDO1 inhibitors have also failed in other tumor types 43 . However, none of these clinical trials were designed to select patients with speci c biomarkers (such high IDO RNA expression levels) for response. Since only a minority of patients (13% in our series) with non-gynecologic cancers have high IDO transcript levels, these results suggest that selection of patients with higher IDO1 levels for IDO1 inhibitor trials may be warranted. Moreover, since almost half of gynecologic cancers express high IDO1 levels, gynecologic malignancies may be a worthwhile target for IDO1 inhibitor studies. The clinical utility of the IDO inhibitor epacadostat in combination with pembrolizumab (NRG GY016) showed a promising ORR in a small cohort of pretreated clear cell ovarian cancer patients (ORR 21%), although the study was terminated prematurely due to lack of drug (Gien et al. IGCS 2022) We also found higher expression of the checkpoint LAG3 in gynecologic cancers as compared to nongynecologic cancers (34.7% versus 20.6%; p=0.008); similar ndings were previously shown in endometrial cancers 44 . The anti-LAG3 relatlimab was recently FDA approved for melanoma 45 and other LAG3 inhibitors are in clinical trials. It is plausible that LAG3 may be an alternative checkpoint upregulated in some of the tumors resistant to anti-PD1/PDL1 agents, and LAG3 inhibitors merit investigation in patients with gynecologic cancers, especially those with high LAG3 expression.
There were several limitations to our study. First, our sample size was relatively small, especially in regard to cervical cancer, with uneven distribution among gynecologic disease sites. Second, since the database was not clinically annotated, we were not able to associate immune biomarker expression levels with immunotherapeutic responses or other clinical oncologic endpoints. Though this data was obtained from a CLIA-licensed laboratory, immune marker RNA from normal tissues were not delineated. Finally, while we were able to delineate differences in immune landscape between PDL1-positive versus -negative cancers, the small number of neoplasms with high TMB or MSI-High disease precluded analysis of these subsets. Despite these limitations, this study provides comprehensive insight into the complexities of the immune landscape in gynecologic malignancies Proteomic and genomic based biomarkers such as MMR, MSI, PDL-1 and TMB have demonstrated e cacy in predicting treatment response to immunotherapies 37,46,47 . RNA sequencing may be an opportunity for discovering a broader cadre of biomarkers to improve the precision and limit the toxicities of this drug class 48, 49 . RNAseq provides valuable information such as transcript abundance, molecular alterations and alternative promoter/splice sites that may be used to predict response or resistance to immune therapies 50,48 . In fact, one study found that almost 90% of patients with (classic) biomarkernegative tumors (MMR pro cient, MSI stable, PDL1 <1%, TMB <10mut/mb), had high levels of other immune marker RNA that were potentially targetable with drugs under active investigation 51 . Overall, our study indicates that patients with gynecologic cancers have complex immune landscapes that differ from patient to patient even within the same histology, but that certain pharmacologically tractable immune markers, such as high levels of IDO1 and LAG3 are present in these cancers.

Declarations
Patient consent for publication: All investigations followed the Institutional Review Board protocol for data collection (Pro le Related Evidence Determining Individualized Cancer Therapy, NCT02478931) and for any investigational interventions for which the patients consented.
Ethics approval: This study involves human subjects, and all samples were obtained following individual informed consent and ethical approval by the Institutional Review Board.
Data availability: Data are available on reasonable request. All data relevant to the study are included in the article or uploaded as online supplemental information.
No other potential con icts to disclose.   Figure 1 and Methods) (N=5) Figure 2 a. Frequency of low RNA expression (<25 percentile rank) among cancer-immunity markers across gynecologic disease sites (see also Supplemental Table 1   The principal component analysis summarizes 51-dimensional data corresponding to different cancerimmunity markers on a two-dimensional eld to analyze the distribution of the samples. The rst dimension is the vector that represents the greatest variation in the data ("Dim1"), which captures 49.1% of the variation in the data. The second dimension ("Dim2") represents 7.2% of the variation in the data.
Together, both vectors capture 56.3% of the variation.
The cluster assignment is based on the site of disease (pink distribution represents ovarian cancer vs. green distribution represents uterine cancer.) Each symbol in the eld represents each case of uterine or ovarian cancer. Patients with cervical cancer were excluded from the analysis due to small numbers. A clear separation in distribution (pink vs. green colors) would suggest that immune marker expression patterns were dependent on disease site. Overlapping distribution would suggest that expression patterns were independent of disease site.
From this plot, the distribution cancer immunity marker expression for patients with uterine and ovarian cancer were largely overlapping, which suggests that the pattern of RNA expression pattern of 51 markers were not associated with disease site. Figure 5 a. Sample immunogram of mean cancer-immunity marker RNA expression rank in patients with gynecologic cancers (N=72) relative control cancer types (n=735) (See also Supplemental Table 1).
As an example, the average RNA expression rank level of immune checkpoint markers in ovarian cancers (green line) is higher than 43% (green circle) of other cancer types. For uterine cancers (brown line), average RNA expression rank level of immune checkpoint markers is higher than 40% (brown circle) of other cancer types. For cervical cancer (yellow line), average RNA expression rank level of immune checkpoint markers is higher than 70% (yellow circle) of other cancer types; immune activating markers (tumor in ltrating lymphocyte markers and pro-in ammatory markers) are also high in cervix cancers compared to other cancer types.
Although a small sample size, this gure demonstrates that cervical cancers have signi cantly higher expression rank levels of immune checkpoints, tumor in ltrating markers, pro-in ammatory markers and T-cell primed markers compared to ovarian and uterine cancers (p<0.0001 for all comparisons).
Gynecologic cancers express similar levels of immune escape markers and macrophage associated markers. There were no signi cant differences in mean expression levels of immune marker categories between uterine and ovarian cancers.
b. Sample immunogram of mean cancer-immunity marker RNA expression rank in gynecologic cancers by PDL1 status IHC (tumor proportion score) (see also Supplemental Table 1) (N=72).
In this immunogram, gynecologic tumors with PDL1 staining >=1% express signi cantly higher levels of pro-in ammatory markers (orange circle) compared to tumors with PDL1 scores of 0% (blue circle) (58 vs. 49%, p=0.0004). There were no other statistically signi cant differences in other immune categories by PDL1 staining. PDL1 staining may be associated with pro-in ammatory markers of the cancer immunity cycle.

Supplementary Files
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